Haplotype trend regression

Usage

htr(y, x, n.sim = 0)

Arguments

y: a vector of phenotype.
x: a haplotype table.
n.sim: the number of permutations.

Value

The returned value is a list containing:

f the F statistic for overall association.
p the p value for overall association.
fv the F statistics for individual haplotypes.
pi the p values for individual haplotypes.

Details

Haplotype trend regression (with permutation)

Note

adapted from emgi.cpp, a pseudorandom number seed will be added on.

References

Zaykin DV, Westfall PH, Young SS, Karnoub MA, Wagner MJ, Ehm MG (2002). “Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals.” Hum Hered, 53(2), 79-91. doi:10.1159/000057986 .

Xie R, Stram DO (2005). “Asymptotic equivalence between two score tests for haplotype-specific risk in general linear models.” Genetic Epidemiology, 29(2), 166-170. doi:10.1002/gepi.20087 .

Author

Dimitri Zaykin, Jing Hua Zhao

Examples

if (FALSE) { # \dontrun{
# 26-10-03
# this is now part of demo
test2<-read.table("test2.dat")
y<-test2[,1]
x<-test2[,-1]
y<-as.matrix(y)
x<-as.matrix(x)
htr.test2<-htr(y,x)
htr.test2
htr.test2<-htr(y,x,n.sim=10)
htr.test2

# 13-11-2003
require(gap.datasets)
data(apoeapoc)
apoeapoc.gc<-gc.em(apoeapoc[,5:8])
y<-apoeapoc$y
for(i in 1:length(y)) if(y[i]==2) y[i]<-1
htr(y,apoeapoc.gc$htrtable)

# 20-8-2008
# part of the example from useR!2008 tutorial by Andrea Foulkes
# It may be used beyond the generalized linear model (GLM) framework
HaploEM <- haplo.em(Geno,locus.label=SNPnames)
HapMat <- HapDesign(HaploEM)
m1 <- lm(Trait~HapMat)
m2 <- lm(Trait~1)
anova(m2,m1)
} # }